Sang Pil Han

An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.

How is the mobile internet different? Search costs and local activities

We explore how Internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile Internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show that (1) ranking effects are higher on mobile phones suggesting higher search costs: links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) the benefit of browsing for geographically close matches is higher on mobile phones: stores located in close proximity to a users home are much more likely to be clicked on mobile phones. Thus, the mobile Internet is somewhat less “Internet-like”: search costs are higher and distance matters more. We speculate on how these cha...

Estimating Demand for Mobile Applications in the New Economy

In 2013, the global mobile app market was estimated at over US

A social network-based inference model for validating customer profile data

50 billion and is expected to grow to

How is the Mobile Internet Different

150 billion in the next two years. In this paper, we build a structural econometric model to quantify the vibrant platform competition between mobile smartphone and tablet apps on the Apple iOS and Google Android platforms and estimate consumer preferences toward different mobile app characteristics. We find that app demand increases with the in-app purchase option wherein a user can complete transactions within the app. On the contrary, app demand decreases with the in-app advertisement option where consumers are shown ads while they are engaging with the app. The direct effects on app revenue from the inclusion of an in-app purchase option and an in-app advertisement option are equivalent to offering a 28% price discount and increasing the price by 8%, respectively. We also find that a price discount strategy results in a greater increase of app demand in Google Play compared with Apple App Store, and app developers can maximize their revenue by providing a 50% discount on their paid apps. Using the estimated demand function, we find that mobile apps have enhanced consumer surplus by approximately

Excessive Dependence on Mobile Social Apps: A Rational Addiction Perspective

33.6 billion annually in the United States, and we discuss various implications for mobile marketing analytics, app pricing, and app design strategies. This paper was accepted by Alok Gupta, special issue on business analytics.

Battle of the Channels: The Impact of Tablets on Digital Commerce

Drawing from the social and relational perspectives, this study offers an innovative conceptualization and operational approach regarding the validation of self-reported customer demographic data, which has become an essential corporate asset for harnessing business intelligence. Specifically, based on social network and homophily paradigms in which individuals have a natural tendency to associate and interact frequently with others with similar characteristics, we constructed a relational inference model to determine the accuracy of self-administered consumer profiles. In addition, to further enhance the reliability of our models prediction capability, we employed the entropy mechanism that minimizes potential biases that may arise from a simple probabilistic approach. To empirically validate the accuracy of our inference framework, we obtained and analyzed over 20 million actual call transactions supplied by one of the largest global telecommunication service providers. The results suggest that our social network-based inference model consistently outperforms other competing mechanisms (e.g., weighted average and simple relational classifier) regardless of the criteria choice (e.g., number of call receivers, call duration, and call frequency), with an accuracy rate of approximately 93 percent. Finally, to confirm the generalizability of our findings, we conducted simulation experiments to validate the robustness of the results in response to variations in parameter values and increases in potential noise in the data. We discuss several implications related to business intelligence for both research and practice, and offer new directions for future studies.

Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework

We explore how internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show (1) Ranking effects are higher on mobile phones suggesting higher cognitive load: Links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) The benefit of browsing for geographically close matches is higher on mobile phones: Stores located in close proximity to a user’s home are much more likely to be clicked on mobile phones. Thus, the mobile internet is somewhat less “internet-like”: search costs are higher and distance matters more. We speculate on how these changes may affect the future direction of internet commerce.

A Dynamic Structural Model of User Learning on the Mobile Internet

Drawing on the rational addiction framework, this study explores the digital vulnerabilities driven by dependence on mobile social apps e.g., social network sites and social games. Rational addicts anticipate the future consequences of their current behaviors and attempt to maximize utility from their intertemporal consumption choices. Conversely, myopic addicts tend toward immediate gratification and fail to fully recognize the future consequences of their current consumption. In lieu of conducting self-report surveys or aggregate-level demand estimation, this research examines addictive behaviors on the basis of consumption quantity at an individual level. To empirically validate rational addiction in the context of social app consumption, we collect and analyze 13-month, individual-level panel data on the weekly app usage of thousands of smartphone users. Results indicate that the average social app user conducts herself in a forward-looking manner and rationally adjusts consumption over time to derive optimal utility. The subgroup analysis, however, indicates that substantial variations in addictiveness and forward-looking propensities exist across demographically diverse groups. For example, addictive behaviors toward social network sites are more myopic in nature among older, less-educated, high-income groups. Additionally, the type of social app moderates the effects of demographic characteristics on the nature of addictive behaviors. We provide implications that policymakers can use to effectively manage mobile addiction problems, with the recommendations focusing on asymmetric social policies e.g., information-and capacity-enhancing measures.

Empirical analysis of the impact of product diversity on long-term performance of recommender systems

The introduction of tablets in online retailing has created an additional touchpoint through which e-commerce firms can interact with consumers. In this paper, we seek to understand and measure the causal impact of tablets on e-commerce sales. In doing so, we examine the complementary and substitution impact of the tablet channel on the smartphone and PC channels. We rely on a unique data set from Alibaba, the largest e-commerce firm in the world, and exploit a natural experiment via the iPad app introduction to empirically identify our results. The results show that users’ adoption of tablets enhanced the overall growth of Alibaba’s e-commerce market, with an annual increase of approximately US